{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WDID5M4EPQCEXHK5LD74RYWLFU","short_pith_number":"pith:WDID5M4E","canonical_record":{"source":{"id":"1807.06899","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-07-18T12:55:59Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"b5332051c930d9850fb96d726d134120d1fa42e7c651bb5f3268480d62e5d249","abstract_canon_sha256":"8cc40ac0ef82a44ac0e920274b1225f7879c60ea9fad1c96f95d664282c63f14"},"schema_version":"1.0"},"canonical_sha256":"b0d03eb3847c044b9d5d58ffc8e2cb2d02322950067fc112ab0aeed5afe55d6a","source":{"kind":"arxiv","id":"1807.06899","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.06899","created_at":"2026-05-18T00:10:25Z"},{"alias_kind":"arxiv_version","alias_value":"1807.06899v1","created_at":"2026-05-18T00:10:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.06899","created_at":"2026-05-18T00:10:25Z"},{"alias_kind":"pith_short_12","alias_value":"WDID5M4EPQCE","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"WDID5M4EPQCEXHK5","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"WDID5M4E","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WDID5M4EPQCEXHK5LD74RYWLFU","target":"record","payload":{"canonical_record":{"source":{"id":"1807.06899","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-07-18T12:55:59Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"b5332051c930d9850fb96d726d134120d1fa42e7c651bb5f3268480d62e5d249","abstract_canon_sha256":"8cc40ac0ef82a44ac0e920274b1225f7879c60ea9fad1c96f95d664282c63f14"},"schema_version":"1.0"},"canonical_sha256":"b0d03eb3847c044b9d5d58ffc8e2cb2d02322950067fc112ab0aeed5afe55d6a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:25.394127Z","signature_b64":"CVU31NwTwyGrJ/6eOw6ku+pFlVJbGN1NyBtrVXy2QJYvOQZEFbLwj4ij04prMtmzBXzYFTxMQ5TaaBOoyGxlAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0d03eb3847c044b9d5d58ffc8e2cb2d02322950067fc112ab0aeed5afe55d6a","last_reissued_at":"2026-05-18T00:10:25.393567Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:25.393567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.06899","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B6Fo7mHGYc/RC0tqdlwepnmVNotFKgkQPuI8kkJHgboE+o7Pm892nhf3n4syWZh7Y9WjxP32MmozCDYjI65aBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T01:07:04.409633Z"},"content_sha256":"93bc655f230dda35ea201d21df1e7ef9faca115dd36da8086f5b4671ef976eb5","schema_version":"1.0","event_id":"sha256:93bc655f230dda35ea201d21df1e7ef9faca115dd36da8086f5b4671ef976eb5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WDID5M4EPQCEXHK5LD74RYWLFU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Gaurav Naithani, Joonas Nikunen, Lars Bramsl{\\o}w, Tuomas Virtanen","submitted_at":"2018-07-18T12:55:59Z","abstract_excerpt":"Mean square error (MSE) has been the preferred choice as loss function in the current deep neural network (DNN) based speech separation techniques. In this paper, we propose a new cost function with the aim of optimizing the extended short time objective intelligibility (ESTOI) measure. We focus on applications where low algorithmic latency ($\\leq 10$ ms) is important. We use long short-term memory networks (LSTM) and evaluate our proposed approach on four sets of two-speaker mixtures from extended Danish hearing in noise (HINT) dataset. We show that the proposed loss function can offer improv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.06899","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bftJvJnZ2KkHLzuAm5t6H8MplfHZaw+ValFgBqRHoNh8zMRjJ8Bh0T/W5C10x6uc0KRChs9dHugdxTgQrxF2Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T01:07:04.410337Z"},"content_sha256":"eacce147189111d8088073fe76effeff00ad9fe100a2614415934017e3cd454c","schema_version":"1.0","event_id":"sha256:eacce147189111d8088073fe76effeff00ad9fe100a2614415934017e3cd454c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WDID5M4EPQCEXHK5LD74RYWLFU/bundle.json","state_url":"https://pith.science/pith/WDID5M4EPQCEXHK5LD74RYWLFU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WDID5M4EPQCEXHK5LD74RYWLFU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T01:07:04Z","links":{"resolver":"https://pith.science/pith/WDID5M4EPQCEXHK5LD74RYWLFU","bundle":"https://pith.science/pith/WDID5M4EPQCEXHK5LD74RYWLFU/bundle.json","state":"https://pith.science/pith/WDID5M4EPQCEXHK5LD74RYWLFU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WDID5M4EPQCEXHK5LD74RYWLFU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WDID5M4EPQCEXHK5LD74RYWLFU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8cc40ac0ef82a44ac0e920274b1225f7879c60ea9fad1c96f95d664282c63f14","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-07-18T12:55:59Z","title_canon_sha256":"b5332051c930d9850fb96d726d134120d1fa42e7c651bb5f3268480d62e5d249"},"schema_version":"1.0","source":{"id":"1807.06899","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.06899","created_at":"2026-05-18T00:10:25Z"},{"alias_kind":"arxiv_version","alias_value":"1807.06899v1","created_at":"2026-05-18T00:10:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.06899","created_at":"2026-05-18T00:10:25Z"},{"alias_kind":"pith_short_12","alias_value":"WDID5M4EPQCE","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"WDID5M4EPQCEXHK5","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"WDID5M4E","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:eacce147189111d8088073fe76effeff00ad9fe100a2614415934017e3cd454c","target":"graph","created_at":"2026-05-18T00:10:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Mean square error (MSE) has been the preferred choice as loss function in the current deep neural network (DNN) based speech separation techniques. In this paper, we propose a new cost function with the aim of optimizing the extended short time objective intelligibility (ESTOI) measure. We focus on applications where low algorithmic latency ($\\leq 10$ ms) is important. We use long short-term memory networks (LSTM) and evaluate our proposed approach on four sets of two-speaker mixtures from extended Danish hearing in noise (HINT) dataset. We show that the proposed loss function can offer improv","authors_text":"Gaurav Naithani, Joonas Nikunen, Lars Bramsl{\\o}w, Tuomas Virtanen","cross_cats":["eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-07-18T12:55:59Z","title":"Deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.06899","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:93bc655f230dda35ea201d21df1e7ef9faca115dd36da8086f5b4671ef976eb5","target":"record","created_at":"2026-05-18T00:10:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8cc40ac0ef82a44ac0e920274b1225f7879c60ea9fad1c96f95d664282c63f14","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-07-18T12:55:59Z","title_canon_sha256":"b5332051c930d9850fb96d726d134120d1fa42e7c651bb5f3268480d62e5d249"},"schema_version":"1.0","source":{"id":"1807.06899","kind":"arxiv","version":1}},"canonical_sha256":"b0d03eb3847c044b9d5d58ffc8e2cb2d02322950067fc112ab0aeed5afe55d6a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b0d03eb3847c044b9d5d58ffc8e2cb2d02322950067fc112ab0aeed5afe55d6a","first_computed_at":"2026-05-18T00:10:25.393567Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:25.393567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CVU31NwTwyGrJ/6eOw6ku+pFlVJbGN1NyBtrVXy2QJYvOQZEFbLwj4ij04prMtmzBXzYFTxMQ5TaaBOoyGxlAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:25.394127Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.06899","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93bc655f230dda35ea201d21df1e7ef9faca115dd36da8086f5b4671ef976eb5","sha256:eacce147189111d8088073fe76effeff00ad9fe100a2614415934017e3cd454c"],"state_sha256":"9f1be51054fcb28942559b79cdff91b157449b88831119a6e8980ff840724c00"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9QVdxMMCuzrigkMOxzaecgvbpAyW1SFk3Pm+ZJwTCcjGRA+OPZc6UNPT2uE0w4rFpz+xrsUnqXL3Vmdu6d40AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T01:07:04.413789Z","bundle_sha256":"858d20fe2cbac055526ef2f26bfb8971540ab79c884e89a0be0d88df9544c149"}}